IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i15p5603-d1202185.html
   My bibliography  Save this article

Extended Recursive Three-Step Filter for Linear Discrete-Time Systems with Dual-Unknown Inputs

Author

Listed:
  • Shigui Dong

    (College of Automation, Qingdao University, Qingdao 266071, China)

  • Na Wang

    (College of Automation, Qingdao University, Qingdao 266071, China
    Shandong Key Laboratory of Industrial Control Technology, Qingdao University, Qingdao 266071, China)

  • Xueyan Wang

    (College of Automation, Qingdao University, Qingdao 266071, China)

  • Zihao Lu

    (College of Automation, Qingdao University, Qingdao 266071, China)

Abstract

This paper proposes two new extended recursive three-step filters for linear discrete systems with dual-unknown inputs, which can simultaneously estimate unknown input and state. Extended recursive three-step filter 1 (ERTSF1) introduces an innovation for obtaining the estimates of the unknown input in the measurement equation, then derives the estimates of the unknown input in the state equation. After that, it uses the already obtained estimates of the dual-unknown inputs to correct the one-step prediction of the state, and finally, it obtains the minimum-variance unbiased estimate of the system state. Extended recursive three-step filter 2 (ERTSF2) establishes a unified innovation feedback model, then applies linear minimum-variance unbiased estimation to obtain the estimates of the system state and the dual-unknown inputs to refine a more concise recursive filter. Numerical Simulation Ex-ample demonstrates the effectiveness and superiority of the two filters in this paper compared with the traditional method. The battery state of charge estimation results demonstrate the effectiveness of ERTSF2 in practical applications.

Suggested Citation

  • Shigui Dong & Na Wang & Xueyan Wang & Zihao Lu, 2023. "Extended Recursive Three-Step Filter for Linear Discrete-Time Systems with Dual-Unknown Inputs," Energies, MDPI, vol. 16(15), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5603-:d:1202185
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/15/5603/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/15/5603/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ming Zhang & Yanshuo Liu & Dezhi Li & Xiaoli Cui & Licheng Wang & Liwei Li & Kai Wang, 2023. "Electrochemical Impedance Spectroscopy: A New Chapter in the Fast and Accurate Estimation of the State of Health for Lithium-Ion Batteries," Energies, MDPI, vol. 16(4), pages 1-16, February.
    2. Ming Zhang & Dongfang Yang & Jiaxuan Du & Hanlei Sun & Liwei Li & Licheng Wang & Kai Wang, 2023. "A Review of SOH Prediction of Li-Ion Batteries Based on Data-Driven Algorithms," Energies, MDPI, vol. 16(7), pages 1-28, March.
    3. Ashikur Rahman & Xianke Lin & Chongming Wang, 2022. "Li-Ion Battery Anode State of Charge Estimation and Degradation Monitoring Using Battery Casing via Unknown Input Observer," Energies, MDPI, vol. 15(15), pages 1-19, August.
    4. Yu Hua & Na Wang & Keyou Zhao, 2021. "Simultaneous Unknown Input and State Estimation for the Linear System with a Rank-Deficient Distribution Matrix," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-11, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Peng Liu & Cheng Liu & Zhenpo Wang & Qiushi Wang & Jinlei Han & Yapeng Zhou, 2023. "A Data-Driven Comprehensive Battery SOH Evaluation and Prediction Method Based on Improved CRITIC-GRA and Att-BiGRU," Sustainability, MDPI, vol. 15(20), pages 1-15, October.
    2. Zizhen Cheng & Li Wang & Yumeng Yang, 2023. "A Hybrid Feature Pyramid CNN-LSTM Model with Seasonal Inflection Month Correction for Medium- and Long-Term Power Load Forecasting," Energies, MDPI, vol. 16(7), pages 1-18, March.
    3. Mahendiran T. Vellingiri & Ibrahim M. Mehedi & Thangam Palaniswamy, 2022. "A Novel Deep Learning-Based State-of-Charge Estimation for Renewable Energy Management System in Hybrid Electric Vehicles," Mathematics, MDPI, vol. 10(2), pages 1-15, January.
    4. Liu, Chunli & Li, Qiang & Wang, Kai, 2021. "State-of-charge estimation and remaining useful life prediction of supercapacitors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    5. Khac Huan Su & Jaeyun Yim & Wonhee Kim & Youngwoo Lee, 2022. "Lyapunov-Based Controller Using Nonlinear Observer for Planar Motors," Mathematics, MDPI, vol. 10(13), pages 1-18, June.
    6. Julan Chen & Guangheng Qi & Kai Wang, 2023. "Synergizing Machine Learning and the Aviation Sector in Lithium-Ion Battery Applications: A Review," Energies, MDPI, vol. 16(17), pages 1-22, August.
    7. Athanasios Ioannis Arvanitidis & Dimitrios Bargiotas & Aspassia Daskalopulu & Dimitrios Kontogiannis & Ioannis P. Panapakidis & Lefteri H. Tsoukalas, 2022. "Clustering Informed MLP Models for Fast and Accurate Short-Term Load Forecasting," Energies, MDPI, vol. 15(4), pages 1-14, February.
    8. Dezhi Li & Dongfang Yang & Liwei Li & Licheng Wang & Kai Wang, 2022. "Electrochemical Impedance Spectroscopy Based on the State of Health Estimation for Lithium-Ion Batteries," Energies, MDPI, vol. 15(18), pages 1-26, September.
    9. Mohamed Hassan & Manwinder Singh & Khalid Hamid & Rashid Saeed & Maha Abdelhaq & Raed Alsaqour, 2022. "Modeling of NOMA-MIMO-Based Power Domain for 5G Network under Selective Rayleigh Fading Channels," Energies, MDPI, vol. 15(15), pages 1-19, August.
    10. Chuanyu Zhang & Chuanxu Cao & Ruiqi Chen & Jiahui Jiang, 2023. "Three-Leg Quasi-Z-Source Inverter with Input Ripple Suppression for Renewable Energy Application," Energies, MDPI, vol. 16(11), pages 1-28, May.
    11. Ming Zhang & Dongfang Yang & Jiaxuan Du & Hanlei Sun & Liwei Li & Licheng Wang & Kai Wang, 2023. "A Review of SOH Prediction of Li-Ion Batteries Based on Data-Driven Algorithms," Energies, MDPI, vol. 16(7), pages 1-28, March.
    12. Gabriele Sordi & Claudio Rabissi & Andrea Casalegno, 2023. "Reliable Thermal-Physical Modeling of Lithium-Ion Batteries: Consistency between High-Frequency Impedance and Ion Transport," Energies, MDPI, vol. 16(12), pages 1-17, June.
    13. Li, Dezhi & Li, Shuo & Zhang, Shubo & Sun, Jianrui & Wang, Licheng & Wang, Kai, 2022. "Aging state prediction for supercapacitors based on heuristic kalman filter optimization extreme learning machine," Energy, Elsevier, vol. 250(C).
    14. Imanol Landa-Medrano & Idoia Urdampilleta & Iker Castrillo & Hans-Jürgen Grande & Iratxe de Meatza & Aitor Eguia-Barrio, 2024. "Making Room for Silicon: Including SiO x in a Graphite-Based Anode Formulation and Harmonization in 1 Ah Cells," Energies, MDPI, vol. 17(7), pages 1-21, March.
    15. Mantas Zelba & Tomas Deveikis & Saulius Gudžius & Audrius Jonaitis & Almantas Bandza, 2023. "Review of Power Control Methods for a Variable Average Power Load Model Designed for a Microgrid with Non-Controllable Renewable Energy Sources," Sustainability, MDPI, vol. 15(11), pages 1-15, June.
    16. Xinwei Sun & Yang Zhang & Yongcheng Zhang & Licheng Wang & Kai Wang, 2023. "Summary of Health-State Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy," Energies, MDPI, vol. 16(15), pages 1-19, July.
    17. Ali Jawad Alrubaie & Mohamed Salem & Khalid Yahya & Mahmoud Mohamed & Mohamad Kamarol, 2023. "A Comprehensive Review of Electric Vehicle Charging Stations with Solar Photovoltaic System Considering Market, Technical Requirements, Network Implications, and Future Challenges," Sustainability, MDPI, vol. 15(10), pages 1-26, May.
    18. Cui, Zhenhua & Kang, Le & Li, Liwei & Wang, Licheng & Wang, Kai, 2022. "A combined state-of-charge estimation method for lithium-ion battery using an improved BGRU network and UKF," Energy, Elsevier, vol. 259(C).
    19. Ning Ma & Huaixian Yin & Kai Wang, 2023. "Prediction of the Remaining Useful Life of Supercapacitors at Different Temperatures Based on Improved Long Short-Term Memory," Energies, MDPI, vol. 16(14), pages 1-14, July.
    20. Xinghao Zhang & Yan Huang & Zhaowei Zhang & Huipin Lin & Yu Zeng & Mingyu Gao, 2022. "A Hybrid Method for State-of-Charge Estimation for Lithium-Ion Batteries Using a Long Short-Term Memory Network Combined with Attention and a Kalman Filter," Energies, MDPI, vol. 15(18), pages 1-26, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5603-:d:1202185. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.